OpenRaiser/Intern-Atlas huggingface dataset:
Character-level Convolutional Network for Text Classification Applied to Chinese Corpus
Venue: arXiv cs.CL 2016
Year: 2016

This article provides an interesting exploration of character-level convolutional neural network solving Chinese corpus text classification problem. We constructed a large-scale Chinese language dataset, and the result shows that character-level convolutional neural network works better on Chinese corpus than its corresponding pinyin format dataset. This is the first time that character-level convolutional neural network applied to text classification problem.
Power Efficiency and Delay Tradeoff of 10GBase-T Energy Efficient Ethernet Protocol
Venue: arXiv cs.NI 2016
Year: 2016

In this paper, we study the power efficiency and delay performance of the IEEE 802.3az Energy Efficient Ethernet (EEE) protocol. A new approach is proposed to analyze the M/G/1 queue with the vacation time that is governed by the arrival process and the parameter τ and N of the BTR strategy. Our key idea is to establish the connection between the vacation time and the arrival process to account for their dependency. We first derive the distribution of the number of arrivals during a vacation time based on an event tree of the BTR strategy, from which we obtain the mean vacation time and the power efficiency. Next, from the condition on the number of arrivals at the end of a vacation period, we derive a generalized P-K formula of the mean delay for EEE systems, and prove that the classical P-K formula of the vacation model is only a special case when the vacation time is independent of the arrival process. Our analysis demonstrates that the τ policy and N policy of the BTR strategy are compensating each other. The τ policy ensures the frame delay is bounded when the traffic load is light, while the N policy ensures the queue length at the end of vacation is bounded when the traffic load is heavy. These results, in turn, provide the rules to select appropriate τ and N. Our analytical results are confirmed by simulations.
Fair and Throughput-Optimal Routing in Multi-Modal Underwater Networks
Venue: arXiv cs.NI 2016
Year: 2016

While acoustic communications have been considered the prominent technology to communicate under water for several years, other technologies are being developed based, e.g., on optical and radio-frequency electro-magnetic waves. Each technology has its own advantages and drawbacks: for example, acoustic signals achieve long communication ranges at order-of-kbit/s bit rate, whereas optical signals offer order-of-Mbit/s transmission rates but only over short transmitter--receiver distances. Such a technological diversity can be leveraged by multi-modal systems, which integrate different technologies and provide intelligence to decide which one should be used at any given time. In this paper, we address a fundamental part of this intelligence by proposing a novel routing protocol for networks of multi-modal nodes. The protocol makes distributed decisions about the flow in each link and over each technology at any given time, in order to advance a packet towards its destination. Our routing protocol prevents bottlenecks and allocates resources fairly to different nodes. We analyze the performance of our protocol via simulations and in a field experiment. The results show that our protocol successfully leverages all technologies to deliver data, even in the presence of imperfect topology information. To permit the reproduction of our results, we share our simulation code.
The Quamoco Product Quality Modelling and Assessment Approach
Venue: arXiv cs.SE 2016
Year: 2016

Published software quality models either provide abstract quality attributes or concrete quality assessments. There are no models that seamlessly integrate both aspects. In the project Quamoco, we built a comprehensive approach with the aim to close this gap. For this, we developed in several iterations a meta quality model specifying general concepts, a quality base model covering the most important quality factors and a quality assessment approach. The meta model introduces the new concept of a product factor, which bridges the gap between concrete measurements and abstract quality aspects. Product factors have measures and instruments to operationalise quality by measurements from manual inspection and tool analysis. The base model uses the ISO 25010 quality attributes, which we refine by 200 factors and 600 measures for Java and C# systems. We found in several empirical validations that the assessment results fit to the expectations of experts for the corresponding systems. The empirical analyses also showed that several of the correlations are statistically significant and that the maintainability part of the base model has the highest correlation, which fits to the fact that this part is the most comprehensive. Although we still see room for extending and improving the base model, it shows a high correspondence with expert opinions and hence is able to form the basis for repeatable and understandable quality assessments in practice.
Practical Secure Aggregation for Federated Learning on User-Held Data
Venue: arXiv cs.CR 2016
Year: 2016

Secure Aggregation protocols allow a collection of mutually distrust parties, each holding a private value, to collaboratively compute the sum of those values without revealing the values themselves. We consider training a deep neural network in the Federated Learning model, using distributed stochastic gradient descent across user-held training data on mobile devices, wherein Secure Aggregation protects each user's model gradient. We design a novel, communication-efficient Secure Aggregation protocol for high-dimensional data that tolerates up to 1/3 users failing to complete the protocol. For 16-bit input values, our protocol offers 1.73x communication expansion for $2^{10}$ users and $2^{20}$-dimensional vectors, and 1.98x expansion for $2^{14}$ users and $2^{24}$ dimensional vectors.
When Saliency Meets Sentiment: Understanding How Image Content Invokes Emotion and Sentiment
Venue: arXiv cs.AI 2016
Year: 2016

Sentiment analysis is crucial for extracting social signals from social media content. Due to the prevalence of images in social media, image sentiment analysis is receiving increasing attention in recent years. However, most existing systems are black-boxes that do not provide insight on how image content invokes sentiment and emotion in the viewers. Psychological studies have confirmed that salient objects in an image often invoke emotions. In this work, we investigate more fine-grained and more comprehensive interaction between visual saliency and visual sentiment. In particular, we partition images in several primary scene-type dimensions, including: open-closed, natural-manmade, indoor-outdoor, and face-noface. Using state of the art saliency detection algorithm and sentiment classification algorithm, we examine how the sentiment of the salient region(s) in an image relates to the overall sentiment of the image. The experiments on a representative image emotion dataset have shown interesting correlation between saliency and sentiment in different scene types and in turn shed light on the mechanism of visual sentiment evocation.
An Overview on Resource Allocation Techniques for Multi-User MIMO Systems
Venue: arXiv cs.IT 2016
Year: 2016

Remarkable research activities and major advances have been occurred over the past decade in multiuser multiple-input multiple-output (MU-MIMO) systems. Several transmission technologies and precoding techniques have been developed in order to exploit the spatial dimension so that simultaneous transmission of independent data streams reuse the same radio resources. The achievable performance of such techniques heavily depends on the channel characteristics of the selected users, the amount of channel knowledge, and how efficiently interference is mitigated. In systems where the total number of receivers is larger than the number of total transmit antennas, user selection becomes a key approach to benefit from multiuser diversity and achieve full multiplexing gain. The overall performance of MU-MIMO systems is a complex joint multi-objective optimization problem since many variables and parameters have to be optimized, including the number of users, the number of antennas, spatial signaling, rate and power allocation, and transmission technique. The objective of this literature survey is to provide a comprehensive overview of the various methodologies used to approach the aforementioned joint optimization task in the downlink of MU-MIMO communication systems.
Asymptotic Performance Analysis of Majority Sentiment Detection in Online Social Networks
Venue: arXiv cs.SI 2016
Year: 2016

We analyze the problem of majority sentiment detection in Online Social Networks (OSN), and relate the detection error probability to the underlying graph of the OSN. Modeling the underlying social network as an Ising Markov random field prior based on a given graph, we show that in the case of the empty graph (independent sentiments) and the chain graph, the detection is always inaccurate, even when the number of users grow to infinity. In the case of the complete graph, the detection is inaccurate if the connection strength is below a certain critical value, while it is asymptotically accurate if the strength is above that critical value, which is analogous to the phase transition phenomenon in statistical physics.
Knowledge Enhanced Hybrid Neural Network for Text Matching
Venue: arXiv cs.CL 2016
Year: 2016

Long text brings a big challenge to semantic matching due to their complicated semantic and syntactic structures. To tackle the challenge, we consider using prior knowledge to help identify useful information and filter out noise to matching in long text. To this end, we propose a knowledge enhanced hybrid neural network (KEHNN). The model fuses prior knowledge into word representations by knowledge gates and establishes three matching channels with words, sequential structures of sentences given by Gated Recurrent Units (GRU), and knowledge enhanced representations. The three channels are processed by a convolutional neural network to generate high level features for matching, and the features are synthesized as a matching score by a multilayer perceptron. The model extends the existing methods by conducting matching on words, local structures of sentences, and global context of sentences. Evaluation results from extensive experiments on public data sets for question answering and conversation show that KEHNN can significantly outperform the-state-of-the-art matching models and particularly improve the performance on pairs with long text.
Toward Multilingual Neural Machine Translation with Universal Encoder and Decoder
Venue: arXiv cs.CL 2016
Year: 2016

In this paper, we present our first attempts in building a multilingual Neural Machine Translation framework under a unified approach. We are then able to employ attention-based NMT for many-to-many multilingual translation tasks. Our approach does not require any special treatment on the network architecture and it allows us to learn minimal number of free parameters in a standard way of training. Our approach has shown its effectiveness in an under-resourced translation scenario with considerable improvements up to 2.6 BLEU points. In addition, the approach has achieved interesting and promising results when applied in the translation task that there is no direct parallel corpus between source and target languages.
Information Theoretic Performance of Periodogram-based CFO Estimation in Massive MU-MIMO Systems
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we study the information theoretic performance of the modified time-reversal maximum ratio combining (TR-MRC) receiver (presented in [9]) with the spatially averaged periodogram-based carrier frequency offset (CFO) estimator (proposed in [7]) in multi-user massive MIMO systems. Our analysis shows that an $\mathcal{O}(\sqrt{M})$ array gain is achieved with this periodogram-based CFO estimator, which is same as the array gain achieved in the ideal/zero CFO scenario ($M$ is the number of base station antennas). Information theoretic performance comparison with the correlation-based CFO estimator for massive MIMO systems (proposed in [6]) reveals that this periodogram-based CFO estimator is more energy efficient in slowly time-varying channels.
CIFAR-10: KNN-based Ensemble of Classifiers
Venue: arXiv cs.CV 2016
Year: 2016

In this paper, we study the performance of different classifiers on the CIFAR-10 dataset, and build an ensemble of classifiers to reach a better performance. We show that, on CIFAR-10, K-Nearest Neighbors (KNN) and Convolutional Neural Network (CNN), on some classes, are mutually exclusive, thus yield in higher accuracy when combined. We reduce KNN overfitting using Principal Component Analysis (PCA), and ensemble it with a CNN to increase its accuracy. Our approach improves our best CNN model from 93.33% to 94.03%.
Detection of north atlantic right whale upcalls using local binary patterns in a two-stage strategy
Venue: arXiv cs.SD 2016
Year: 2016

In this paper, we investigate the effectiveness of two-stage classification strategies in detecting north Atlantic right whale upcalls. Time-frequency measurements of data from passive acoustic monitoring devices are evaluated as images. Vocalization spectrograms are preprocessed for noise reduction and tone removal. First stage of the algorithm eliminates non-upcalls by an energy detection algorithm. In the second stage, two sets of features are extracted from the remaining signals using contour-based and texture based methods. The former is based on extraction of time-frequency features from upcall contours, and the latter employs a Local Binary Pattern operator to extract distinguishing texture features of the upcalls. Subsequently evaluation phase is carried out by using several classifiers to assess the effectiveness of both the contour-based and texture-based features for upcall detection. Experimental results with the data set provided by the Cornell University Bioacoustics Research Program reveal that classifiers show accuracy improvements of 3% to 4% when using LBP features over time-frequency features. Classifiers such as the Linear Discriminant Analysis, Support Vector Machine, and TreeBagger achieve high upcall detection rates with LBP features.
End-to-End Neural Sentence Ordering Using Pointer Network
Venue: arXiv cs.CL 2016
Year: 2016

Sentence ordering is one of important tasks in NLP. Previous works mainly focused on improving its performance by using pair-wise strategy. However, it is nontrivial for pair-wise models to incorporate the contextual sentence information. In addition, error prorogation could be introduced by using the pipeline strategy in pair-wise models. In this paper, we propose an end-to-end neural approach to address the sentence ordering problem, which uses the pointer network (Ptr-Net) to alleviate the error propagation problem and utilize the whole contextual information. Experimental results show the effectiveness of the proposed model. Source codes and dataset of this paper are available.
Iterative Orthogonal Feature Projection for Diagnosing Bias in Black-Box Models
Venue: arXiv cs.LG 2016
Year: 2016

Predictive models are increasingly deployed for the purpose of determining access to services such as credit, insurance, and employment. Despite potential gains in productivity and efficiency, several potential problems have yet to be addressed, particularly the potential for unintentional discrimination. We present an iterative procedure, based on orthogonal projection of input attributes, for enabling interpretability of black-box predictive models. Through our iterative procedure, one can quantify the relative dependence of a black-box model on its input attributes.The relative significance of the inputs to a predictive model can then be used to assess the fairness (or discriminatory extent) of such a model.
An integrated Graphical User Interface for Debugging Answer Set Programs
Venue: arXiv cs.AI 2016
Year: 2016

Answer Set Programming (ASP) is an expressive knowledge representation and reasoning framework. Due to its rather simple syntax paired with high-performance solvers, ASP is interesting for industrial applications. However, to err is human and thus debugging is an important activity during the development process. Therefore, tools for debugging non-ground answer set programs are needed. In this paper, we present a new graphical debugging interface for non-ground answer set programs. The tool is based on the recently-introduced DWASP approach for debugging and it simplifies the interaction with the debugger. Furthermore, the debugging interface is integrated in ASPIDE, a rich IDE for answer set programs. With our extension ASPIDE turns into a full-fledged IDE by offering debugging support.
Sparse Control for Dynamic Movement Primitives
Venue: arXiv cs.SY 2016
Year: 2016

This paper describes the use of spatially-sparse inputs to influence global changes in the behavior of Dynamic Movement Primitives (DMPs). The dynamics of DMPs are analyzed through the framework of contraction theory as networked hierarchies of contracting or transversely contracting systems. Within this framework, sparsely-inhibited rhythmic DMPs (SI-RDMPs) are introduced to both inhibit or enable rhythmic primitives through spatially-sparse modification of the DMP dynamics. SI-RDMPs are demonstrated in experiments to manage start-stop transitions for walking experiments with the MIT Cheetah. New analytical results on the coupling of oscillators with diverse natural frequencies are also discussed.
Well-Typed Languages are Sound
Venue: arXiv cs.PL 2016
Year: 2016

Type soundness is an important property of modern programming languages. In this paper we explore the idea that "well-typed languages are sound": the idea that the appropriate typing discipline over language specifications guarantees that the language is type sound. We instantiate this idea for a certain class of languages defined using small step operational semantics by ensuring the progress and preservation theorems. Our first contribution is a syntactic discipline for organizing and restricting language specifications so that they automatically satisfy the progress theorem. This discipline is not novel but makes explicit the way expert language designers have been organizing a certain class of languages for long time. We give a formal account of this discipline by representing language specifications as (higher-order) logic programs and by giving a meta type system over that collection of formulas. Our second contribution is a methodology and meta type system for guaranteeing that languages satisfy the preservation theorem. Ultimately, we proved that language specifications that conform to our meta type systems are guaranteed to be type sound. We have implemented these ideas in the TypeSoundnessCertifier, a tool that takes language specifications in the form of logic programs and type checks them according to our meta type systems. For those languages that pass our type checker, our tool automatically produces a proof of type soundness that can be machine-checked by the Abella proof assistant. For those languages that fail our type checker, the tool pinpoints the design mistakes that hinder type soundness. We have applied the TypeSoundnessCertifier to a large number of programming languages, including those with recursive types, polymorphism, letrec, exceptions, lists and other common types and operators.
Driving CDCL Search
Venue: arXiv cs.AI 2016
Year: 2016

The CDCL algorithm is the leading solution adopted by state-of-the-art solvers for SAT, SMT, ASP, and others. Experiments show that the performance of CDCL solvers can be significantly boosted by embedding domain-specific heuristics, especially on large real-world problems. However, a proper integration of such criteria in off-the-shelf CDCL implementations is not obvious. In this paper, we distill the key ingredients that drive the search of CDCL solvers, and propose a general framework for designing and implementing new heuristics. We implemented our strategy in an ASP solver, and we experimented on two industrial domains. On hard problem instances, state-of-the-art implementations fail to find any solution in acceptable time, whereas our implementation is very successful and finds all solutions.
Simple Yet Effective Methods for Large-Scale Scholarly Publication Ranking
Venue: arXiv cs.IR 2016
Year: 2016

With the growing amount of published research, automatic evaluation of scholarly publications is becoming an important task. In this paper we address this problem and present a simple and transparent approach for evaluating the importance of scholarly publications. Our method has been ranked among the top performers in the WSDM Cup 2016 Challenge. The first part of this paper describes our method. In the second part we present potential improvements to the method and analyse the evaluation setup which was provided during the challenge. Finally, we discuss future challenges in automatic evaluation of papers including the use of full-texts based evaluation methods.
Incremental and Fully Dynamic Subgraph Connectivity For Emergency Planning
Venue: arXiv cs.DS 2016
Year: 2016

During the last 10 years it has become popular to study dynamic graph problems in a emergency planning or sensitivity setting: Instead of considering the general fully dynamic problem, we only have to process a single batch update of size $d$; after the update we have to answer queries. In this paper, we consider the dynamic subgraph connectivity problem with sensitivity $d$: We are given a graph of which some vertices are activated and some are deactivated. After that we get a single update in which the states of up to d vertices are changed. Then we get a sequence of connectivity queries in the subgraph of activated vertices. We present the first fully dynamic algorithm for this problem which has an update and query time only slightly worse than the best decremental algorithm. In addition, we present the first incremental algorithm which is tight with respect to the best known conditional lower bound; moreover, the algorithm is simple and we believe it is implementable and efficient in practice.
Generalisation and Sharing in Triplet Convnets for Sketch based Visual Search
Venue: arXiv cs.CV 2016
Year: 2016

We propose and evaluate several triplet CNN architectures for measuring the similarity between sketches and photographs, within the context of the sketch based image retrieval (SBIR) task. In contrast to recent fine-grained SBIR work, we study the ability of our networks to generalise across diverse object categories from limited training data, and explore in detail strategies for weight sharing, pre-processing, data augmentation and dimensionality reduction. We exceed the performance of pre-existing techniques on both the Flickr15k category level SBIR benchmark by $18\%$, and the TU-Berlin SBIR benchmark by $\sim10 \mathcal{T}_b$, when trained on the 250 category TU-Berlin classification dataset augmented with 25k corresponding photographs harvested from the Internet.